{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forecasting Electricity Consumption with Support Vector Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Created by Justin Elszasz (see [The Training Set](http://www.thetrainingset.com))\n",
    "\n",
    "This notebook describes the process of training a support vector regression model for predicting electricity consumption based on several weather variables, the hour of the day, and whether the day was a weekend/holiday/work-from-home-day or just a normal weekday.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick notes on support vector machines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Support vector machines are a form of machine learning that can be used for classification or regression.  Put as simply as possible, support vector machines find the optimal to line or plane dividing two groups of data or, in the case of regression, the optimal path that characterizes the trend within a tolerance.  \n",
    "\n",
    "For classification, the algorithm minimizes the risk of misclassifying the data.  \n",
    "\n",
    "For regression, the algorithm minimizes the risk that the regression model doesn't capture a data point within some acceptable tolerance.\n",
    "\n",
    "For more information, Google.  Tons of free information and tutorials out there.  Really solid references for machine learning and pattern classification are [\"Pattern Recognition and Machine Learning\" by Christopher M. Bishop](http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=sr_1_sc_1?ie=UTF8&qid=1452695835&sr=8-1-spell&keywords=pattern+classification+bisohp) and [\"Pattern Classification\" by Richard Duda et al](http://www.amazon.com/Pattern-Classification-2nd-Computer-Manual/dp/0471703508/ref=sr_1_1?ie=UTF8&qid=1452695806&sr=8-1&keywords=pattern+classification+duda)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](other/Support_Vector_Classification.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Not covered in this notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Seasonality\n",
    "* Feature selection\n",
    "* Overfitting, crossvalidation.\n",
    "* My new(er) apartment!  (Much more data, been there about 1 year and 8 months)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing some packages and data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd # Fantastic for data analysis, especially time series!\n",
    "import numpy as np # matrix and linear algebra stuff, similar to MATLAB\n",
    "from matplotlib import pyplot as plt # plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Scikit-learn](http://scikit-learn.org/stable/) is one of the big machine learning packages out there for Python.  They have fantastic documentation, tutorials, and examples on their website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "from sklearn import cross_validation\n",
    "from sklearn import preprocessing as pre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random plug for better data visualization here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# some nice colors - like Dad always said, it's better to look good than to be good\n",
    "gray_light = '#d4d4d2'\n",
    "gray_med = '#737373'\n",
    "red_orange = '#ff3700'\n",
    "\n",
    "fontsize = 18"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data I've used in this model are obtained through Baltimore Gas and Electric and Opower from a smart meter installed at my apartment in Baltimore, MD.  The smart meter data are exported from the BGE website and conform to the Green Button standard created by NIST.  \n",
    "\n",
    "The \"USAGE\" field gives the number of kilowatt-hours of electricity used in that hour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start of electricity data:  2014-01-18 00:00:00\n",
      "End of electricity data:  2014-04-30 23:00:00\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>COST</th>\n",
       "      <th>UNITS</th>\n",
       "      <th>USAGE</th>\n",
       "      <th>timestamp_end</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-18 00:00:00</th>\n",
       "      <td> NaN</td>\n",
       "      <td> kWh</td>\n",
       "      <td> 1.13</td>\n",
       "      <td> 2014-01-18 00:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-18 01:00:00</th>\n",
       "      <td> NaN</td>\n",
       "      <td> kWh</td>\n",
       "      <td> 0.98</td>\n",
       "      <td> 2014-01-18 01:59:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-18 02:00:00</th>\n",
       "      <td> NaN</td>\n",
       "      <td> kWh</td>\n",
       "      <td> 0.94</td>\n",
       "      <td> 2014-01-18 02:59:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    COST UNITS  USAGE        timestamp_end\n",
       "timestamp                                                 \n",
       "2014-01-18 00:00:00  NaN   kWh   1.13  2014-01-18 00:59:00\n",
       "2014-01-18 01:00:00  NaN   kWh   0.98  2014-01-18 01:59:00\n",
       "2014-01-18 02:00:00  NaN   kWh   0.94  2014-01-18 02:59:00"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec = pd.read_csv('data/elec_hourly_oldApt_2014-04-30.csv', parse_dates=True, index_col=0)\n",
    "print 'Start of electricity data: ', min(elec.index)\n",
    "print 'End of electricity data: ', max(elec.index)\n",
    "elec.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weather data were culled from Weather Underground's API (in another Python script)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start of weather data:  2014-01-18 00:00:00\n",
      "End of weather data:  2014-04-30 23:00:00\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hum</th>\n",
       "      <th>precipm</th>\n",
       "      <th>tempm</th>\n",
       "      <th>wspdm</th>\n",
       "      <th>tempF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01 01:00:00</th>\n",
       "      <td> 45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.9</td>\n",
       "      <td>  5.6</td>\n",
       "      <td> 25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 02:00:00</th>\n",
       "      <td> 43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.9</td>\n",
       "      <td> 14.8</td>\n",
       "      <td> 25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 03:00:00</th>\n",
       "      <td> 46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.4</td>\n",
       "      <td> 14.8</td>\n",
       "      <td> 24.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 04:00:00</th>\n",
       "      <td> 46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.4</td>\n",
       "      <td> 14.8</td>\n",
       "      <td> 24.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 05:00:00</th>\n",
       "      <td> 48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.4</td>\n",
       "      <td>  0.0</td>\n",
       "      <td> 24.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     hum  precipm  tempm  wspdm  tempF\n",
       "timestamp                                             \n",
       "2014-01-01 01:00:00   45      NaN   -3.9    5.6   25.0\n",
       "2014-01-01 02:00:00   43      NaN   -3.9   14.8   25.0\n",
       "2014-01-01 03:00:00   46      NaN   -4.4   14.8   24.1\n",
       "2014-01-01 04:00:00   46      NaN   -4.4   14.8   24.1\n",
       "2014-01-01 05:00:00   48      NaN   -4.4    0.0   24.1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather = pd.read_csv('data/weather_2015-02-01.csv', parse_dates=True, index_col=0)\n",
    "print 'Start of weather data: ', min(elec.index)\n",
    "print 'End of weather data: ', max(elec.index)\n",
    "weather.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-processing(!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge electricity and weather"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to **merge** the electricity data and weather data into one dataframe and get rid of extraneous information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total number of observations:  2470 \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hum</th>\n",
       "      <th>tempF</th>\n",
       "      <th>USAGE</th>\n",
       "      <th>timestamp_end</th>\n",
       "      <th>wspdMPH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-18 00:00:00</th>\n",
       "      <td> 56</td>\n",
       "      <td> 39.2</td>\n",
       "      <td> 1.13</td>\n",
       "      <td> 2014-01-18 00:59:00</td>\n",
       "      <td> 4.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-18 01:00:00</th>\n",
       "      <td> 61</td>\n",
       "      <td> 39.2</td>\n",
       "      <td> 0.98</td>\n",
       "      <td> 2014-01-18 01:59:00</td>\n",
       "      <td> 4.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-18 02:00:00</th>\n",
       "      <td> 61</td>\n",
       "      <td> 39.2</td>\n",
       "      <td> 0.94</td>\n",
       "      <td> 2014-01-18 02:59:00</td>\n",
       "      <td> 4.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-18 03:00:00</th>\n",
       "      <td> 65</td>\n",
       "      <td> 39.2</td>\n",
       "      <td> 1.11</td>\n",
       "      <td> 2014-01-18 03:59:00</td>\n",
       "      <td> 4.588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-18 04:00:00</th>\n",
       "      <td> 70</td>\n",
       "      <td> 39.2</td>\n",
       "      <td> 1.34</td>\n",
       "      <td> 2014-01-18 04:59:00</td>\n",
       "      <td> 4.588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     hum  tempF  USAGE        timestamp_end  wspdMPH\n",
       "timestamp                                                           \n",
       "2014-01-18 00:00:00   56   39.2   1.13  2014-01-18 00:59:00    4.588\n",
       "2014-01-18 01:00:00   61   39.2   0.98  2014-01-18 01:59:00    4.588\n",
       "2014-01-18 02:00:00   61   39.2   0.94  2014-01-18 02:59:00    4.588\n",
       "2014-01-18 03:00:00   65   39.2   1.11  2014-01-18 03:59:00    4.588\n",
       "2014-01-18 04:00:00   70   39.2   1.34  2014-01-18 04:59:00    4.588"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge into one Pandas dataframe\n",
    "elec_and_weather = pd.merge(weather, elec, left_index=True, right_index=True)\n",
    "\n",
    "# Remove unnecessary fields from dataframe\n",
    "del elec_and_weather['tempm'], elec_and_weather['COST'], elec_and_weather['UNITS']\n",
    "del elec_and_weather['precipm']\n",
    "\n",
    "# Convert windspeed to MPH for my feeble brain to interpret\n",
    "elec_and_weather['wspdMPH'] = elec_and_weather['wspdm'] * 0.62\n",
    "del elec_and_weather['wspdm']\n",
    "\n",
    "print '\\nTotal number of observations: ', len(elec_and_weather),'\\n'\n",
    "elec_and_weather.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+unC7MQNDkPP0DAp11lxhkF5mp2FkkfN4oThrpZIpptJuZ216Ah78CyxZ0b59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\nMScDd99oXnkcBDXFXoHqRyeVtXc5PXd7kgbJOlSzJjt+RqxZU9MgZdO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KC1TfF8pVkHAj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rzTCHQRrgGAapK2t6GOTYFvSkitizqEWDFwuuJUlcH3m+T9WmGLhfhwDyRrJWrawddpht74jH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa241198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[14,8])\n",
    "\n",
    "ax1 = elec_and_weather['USAGE'].plot(label='asdf', color='b', linewidth=0.5)\n",
    "ax2 = elec_and_weather['tempF'].plot(secondary_y = True, label='cew', color=red_orange, linewidth=0.5)\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_ylabel('Usage (kilowatt-hours)', fontsize=fontsize, color = 'b')\n",
    "ax2.set_ylabel('Outdoor Temperature (degrees F)', fontsize=fontsize, color=red_orange)\n",
    "ax1.grid('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xa4a8550>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa39f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "elec_and_weather['USAGE'].hist(bins=20)\n",
    "xlabel('Electricity Usage (kWh)', fontsize=fontsize)\n",
    "ylabel('Frequency', fontsize=fontsize)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to distinguish typical weekdays from any weekends, holidays and days I worked from home.  I don't think any of those categories have enough of their own examples in order to qualify them as their own set so for now all normal weekdays are 0, all holidays, weekends, and work-from-home days are 1's.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical variable: weekdays v. weekends/holidays/working-from-home"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jelszasz\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\ipykernel\\__main__.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\jelszasz\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\ipykernel\\__main__.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\jelszasz\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\ipykernel\\__main__.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hum</th>\n",
       "      <th>tempF</th>\n",
       "      <th>USAGE</th>\n",
       "      <th>timestamp_end</th>\n",
       "      <th>wspdMPH</th>\n",
       "      <th>Atypical_Day</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-18 00:00:00</th>\n",
       "      <td> 56</td>\n",
       "      <td> 39.2</td>\n",
       "      <td> 1.13</td>\n",
       "      <td> 2014-01-18 00:59:00</td>\n",
       "      <td> 4.588</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-18 01:00:00</th>\n",
       "      <td> 61</td>\n",
       "      <td> 39.2</td>\n",
       "      <td> 0.98</td>\n",
       "      <td> 2014-01-18 01:59:00</td>\n",
       "      <td> 4.588</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-18 02:00:00</th>\n",
       "      <td> 61</td>\n",
       "      <td> 39.2</td>\n",
       "      <td> 0.94</td>\n",
       "      <td> 2014-01-18 02:59:00</td>\n",
       "      <td> 4.588</td>\n",
       "      <td> 1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     hum  tempF  USAGE        timestamp_end  wspdMPH  \\\n",
       "timestamp                                                              \n",
       "2014-01-18 00:00:00   56   39.2   1.13  2014-01-18 00:59:00    4.588   \n",
       "2014-01-18 01:00:00   61   39.2   0.98  2014-01-18 01:59:00    4.588   \n",
       "2014-01-18 02:00:00   61   39.2   0.94  2014-01-18 02:59:00    4.588   \n",
       "\n",
       "                     Atypical_Day  \n",
       "timestamp                          \n",
       "2014-01-18 00:00:00             1  \n",
       "2014-01-18 01:00:00             1  \n",
       "2014-01-18 02:00:00             1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Set weekends and holidays to 1, otherwise 0\n",
    "elec_and_weather['Atypical_Day'] = np.zeros(len(elec_and_weather['USAGE']))\n",
    "\n",
    "# Weekends\n",
    "elec_and_weather['Atypical_Day'][(elec_and_weather.index.dayofweek==5)|(elec_and_weather.index.dayofweek==6)] = 1\n",
    "\n",
    "# Holidays, days I worked from home\n",
    "holidays = ['2014-01-01','2014-01-20']\n",
    "work_from_home = ['2014-01-21','2014-02-13','2014-03-03','2014-04-04']\n",
    "\n",
    "for i in range(len(holidays)):\n",
    "    elec_and_weather['Atypical_Day'][elec_and_weather.index.date==np.datetime64(holidays[i])] = 1\n",
    "\n",
    "for i in range(len(work_from_home)):\n",
    "    elec_and_weather['Atypical_Day'][elec_and_weather.index.date==np.datetime64(work_from_home[i])] = 1\n",
    " \n",
    "elec_and_weather.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More categorical variables: day of week, hour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this case each hour of the day is a categorical variable, not continuous. The doing the analysis will require \"yes\" or \"no\" corresponding to each hour of the day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jelszasz\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\ipykernel\\__main__.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "2014-01-18 00:00:00    0\n",
       "2014-01-18 01:00:00    0\n",
       "2014-01-18 02:00:00    0\n",
       "2014-01-18 03:00:00    1\n",
       "2014-01-18 04:00:00    0\n",
       "2014-01-18 05:00:00    0\n",
       "Name: 3, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new column for each hour of day, assign 1 if index.hour is corresponding hour of column, 0 otherwise\n",
    "\n",
    "for i in range(0,24):\n",
    "    elec_and_weather[i] = np.zeros(len(elec_and_weather['USAGE']))\n",
    "    elec_and_weather[i][elec_and_weather.index.hour==i] = 1\n",
    "    \n",
    "# Example 3am\n",
    "elec_and_weather[3][:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time-series: need to append with historical window of previous electricity demand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this is a time series, if we want to predict the next hour's energy consumption, any given X vector/Y target pair in the training data should provide the current hour's electricity use (Y value, or target) with the previous hour's (or however many past hours are used) weather data and usage (X vector)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jelszasz\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\ipykernel\\__main__.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hum</th>\n",
       "      <th>tempF</th>\n",
       "      <th>USAGE</th>\n",
       "      <th>timestamp_end</th>\n",
       "      <th>wspdMPH</th>\n",
       "      <th>Atypical_Day</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>...</th>\n",
       "      <th>USAGE_t-3</th>\n",
       "      <th>USAGE_t-4</th>\n",
       "      <th>USAGE_t-5</th>\n",
       "      <th>USAGE_t-6</th>\n",
       "      <th>USAGE_t-7</th>\n",
       "      <th>USAGE_t-8</th>\n",
       "      <th>USAGE_t-9</th>\n",
       "      <th>USAGE_t-10</th>\n",
       "      <th>USAGE_t-11</th>\n",
       "      <th>USAGE_t-12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-18 13:00:00</th>\n",
       "      <td> 75</td>\n",
       "      <td> 37.4</td>\n",
       "      <td> 1.60</td>\n",
       "      <td> 2014-01-18 13:59:00</td>\n",
       "      <td> 4.588</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 1.54</td>\n",
       "      <td> 1.56</td>\n",
       "      <td> 1.44</td>\n",
       "      <td> 1.26</td>\n",
       "      <td> 1.33</td>\n",
       "      <td> 1.16</td>\n",
       "      <td> 1.34</td>\n",
       "      <td> 1.11</td>\n",
       "      <td> 0.94</td>\n",
       "      <td> 0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-18 14:00:00</th>\n",
       "      <td> 75</td>\n",
       "      <td> 37.4</td>\n",
       "      <td> 1.68</td>\n",
       "      <td> 2014-01-18 14:59:00</td>\n",
       "      <td> 4.588</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 1.87</td>\n",
       "      <td> 1.54</td>\n",
       "      <td> 1.56</td>\n",
       "      <td> 1.44</td>\n",
       "      <td> 1.26</td>\n",
       "      <td> 1.33</td>\n",
       "      <td> 1.16</td>\n",
       "      <td> 1.34</td>\n",
       "      <td> 1.11</td>\n",
       "      <td> 0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-18 15:00:00</th>\n",
       "      <td> 60</td>\n",
       "      <td> 37.4</td>\n",
       "      <td> 1.43</td>\n",
       "      <td> 2014-01-18 15:59:00</td>\n",
       "      <td> 4.588</td>\n",
       "      <td> 1</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td> 0</td>\n",
       "      <td>...</td>\n",
       "      <td> 1.88</td>\n",
       "      <td> 1.87</td>\n",
       "      <td> 1.54</td>\n",
       "      <td> 1.56</td>\n",
       "      <td> 1.44</td>\n",
       "      <td> 1.26</td>\n",
       "      <td> 1.33</td>\n",
       "      <td> 1.16</td>\n",
       "      <td> 1.34</td>\n",
       "      <td> 1.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     hum  tempF  USAGE        timestamp_end  wspdMPH  \\\n",
       "timestamp                                                              \n",
       "2014-01-18 13:00:00   75   37.4   1.60  2014-01-18 13:59:00    4.588   \n",
       "2014-01-18 14:00:00   75   37.4   1.68  2014-01-18 14:59:00    4.588   \n",
       "2014-01-18 15:00:00   60   37.4   1.43  2014-01-18 15:59:00    4.588   \n",
       "\n",
       "                     Atypical_Day  0  1  2  3     ...      USAGE_t-3  \\\n",
       "timestamp                                         ...                  \n",
       "2014-01-18 13:00:00             1  0  0  0  0     ...           1.54   \n",
       "2014-01-18 14:00:00             1  0  0  0  0     ...           1.87   \n",
       "2014-01-18 15:00:00             1  0  0  0  0     ...           1.88   \n",
       "\n",
       "                     USAGE_t-4  USAGE_t-5  USAGE_t-6  USAGE_t-7  USAGE_t-8  \\\n",
       "timestamp                                                                    \n",
       "2014-01-18 13:00:00       1.56       1.44       1.26       1.33       1.16   \n",
       "2014-01-18 14:00:00       1.54       1.56       1.44       1.26       1.33   \n",
       "2014-01-18 15:00:00       1.87       1.54       1.56       1.44       1.26   \n",
       "\n",
       "                     USAGE_t-9  USAGE_t-10  USAGE_t-11  USAGE_t-12  \n",
       "timestamp                                                           \n",
       "2014-01-18 13:00:00       1.34        1.11        0.94        0.98  \n",
       "2014-01-18 14:00:00       1.16        1.34        1.11        0.94  \n",
       "2014-01-18 15:00:00       1.33        1.16        1.34        1.11  \n",
       "\n",
       "[3 rows x 42 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add historic usage to each X vector\n",
    "\n",
    "# Set number of hours prediction is in advance\n",
    "n_hours_advance = 1\n",
    "\n",
    "# Set number of historic hours used\n",
    "n_hours_window = 12\n",
    "\n",
    "\n",
    "for k in range(n_hours_advance,n_hours_advance+n_hours_window):\n",
    "    \n",
    "    elec_and_weather['USAGE_t-%i'% k] = np.zeros(len(elec_and_weather['USAGE']))\n",
    "    #elec_and_weather['tempF_t-%i'% k] = np.zeros(len(elec_and_weather['tempF']))\n",
    "    #elec_and_weather['hum_t-%i'% k] = np.zeros(len(elec_and_weather['hum']))\n",
    "    #elec_and_weather['wspdMPH_t-%i'% k] = np.zeros(len(elec_and_weather['wspdMPH']))\n",
    "    \n",
    "    \n",
    "for i in range(n_hours_advance+n_hours_window,len(elec_and_weather['USAGE'])):\n",
    "    \n",
    "    for j in range(n_hours_advance,n_hours_advance+n_hours_window):\n",
    "        \n",
    "        elec_and_weather['USAGE_t-%i'% j][i] = elec_and_weather['USAGE'][i-j]\n",
    "        #elec_and_weather['tempF_t-%i'% j][i] = elec_and_weather['tempF'][i-j]\n",
    "        #elec_and_weather['wspdMPH_t-%i'% j][i] = elec_and_weather['wspdMPH'][i-j]\n",
    "        #elec_and_weather['hum_t-%i'% j][i] = elec_and_weather['hum'][i-j]\n",
    "\n",
    "elec_and_weather = elec_and_weather.ix[n_hours_advance+n_hours_window:]\n",
    "        \n",
    "elec_and_weather.head(3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Breaking into training and testing periods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this is time series data, it makes more sense to define training and testing periods, rather than random sporadic data points.  If it weren't a time series, we could select a random sample to segregate for a testing set.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Define training and testing periods\n",
    "train_start = '18-jan-2014'\n",
    "train_end = '24-march-2014'\n",
    "test_start = '25-march-2014'\n",
    "test_end = '31-march-2014'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Split up into training and testing sets (still in Pandas dataframes)\n",
    "\n",
    "X_train_df = elec_and_weather[train_start:train_end]\n",
    "del X_train_df['USAGE']\n",
    "del X_train_df['timestamp_end']\n",
    "#del X_train_df['hum']\n",
    "#del X_train_df['tempF']\n",
    "del X_train_df['wspdMPH']\n",
    "\n",
    "y_train_df = elec_and_weather['USAGE'][train_start:train_end]\n",
    "\n",
    "X_test_df = elec_and_weather[test_start:test_end]\n",
    "del X_test_df['USAGE']\n",
    "del X_test_df['timestamp_end']\n",
    "#del X_test_df['hum']\n",
    "#del X_test_df['tempF']\n",
    "del X_test_df['wspdMPH']\n",
    "\n",
    "y_test_df = elec_and_weather['USAGE'][test_start:test_end]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Printed out the training set to a csv in case anyone wants to take a better look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_train_df.to_csv('output/training_set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of observations in the training set:  1569\n"
     ]
    }
   ],
   "source": [
    "N_train = len(X_train_df[0])\n",
    "print 'Number of observations in the training set: ', N_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scikit-learn package takes in Numpy arrays, not Pandas DataFrames, so we need to convert."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Numpy arrays for sklearn\n",
    "X_train = np.array(X_train_df)\n",
    "X_test = np.array(X_test_df)\n",
    "y_train = np.array(y_train_df)\n",
    "y_test = np.array(y_test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scaling variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All variables need scaled.  The algorithm doesn't know anything about what scales each of the variables are on.  In other words, a value of 73 in the temperature column would look like it dominates over a 0.3 in the previous hour kWh usage since the actual values are so different.  StandardScaler() from the preprocessing module in sklearn removes the mean from each variable and scales to unit variance.  When the model is trained on the scaled data the model does the job of determining which variables are more influential rather than the arbitrary scale/order of magnitude predetermining that influence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing as pre\n",
    "scaler = pre.StandardScaler().fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the SVR model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model to the training data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing R^2 = 0.788\n"
     ]
    }
   ],
   "source": [
    "SVR_model = svm.SVR(kernel='rbf',C=100,gamma=.001).fit(X_train_scaled,y_train)\n",
    "print 'Testing R^2 =', round(SVR_model.score(X_test_scaled,y_test),3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction and testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate next-hour forecast (predict!)  We set aside a test dataset, so we'll use all the input variables (scaled appropriately) to predict the \"Y\" target values (the usage at the next hour)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Use SVR model to calculate predicted next-hour usage\n",
    "predict_y_array = SVR_model.predict(X_test_scaled)\n",
    "\n",
    "# Put it in a Pandas dataframe for ease of use\n",
    "predict_y = pd.DataFrame(predict_y_array,columns=['USAGE'])\n",
    "predict_y.index = X_test_df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot time series of actual and predicted electricity demand over the testing period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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phEaHosD9iYwxHwGFwHzgZmAUsAj4fcsMs31ww9Dhw4fxer1tPBoJR6jpc625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WAAAg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\nRERERESn/RdlZJ7YwH3XVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbe6d7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the predicted values and actual\n",
    "import matplotlib.dates as dates\n",
    "\n",
    "plot_start = test_start\n",
    "plot_end = test_end\n",
    "\n",
    "fig = plt.figure(figsize=[14,6])\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(y_test_df.index,y_test_df,color='k',linewidth=2)\n",
    "plt.plot(predict_y.index,predict_y,color=red_orange,linewidth=2)\n",
    "plt.ylabel('Electricity Usage (kWh)', fontsize=fontsize)\n",
    "plt.ylim([0,2])\n",
    "plt.legend(['Actual','Predicted'],loc='best', fontsize=fontsize)\n",
    "ax.xaxis.set_major_formatter(dates.DateFormatter('%b %d'))\n",
    "#fig.savefig('SVM_predict_TS.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampled results to daily kilowatt-hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4BRgOTAd2AT8GbgEmECxTISIiIlLO0qWLY6ure/cesSdj7kk394lVlOUrugIX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa4bc198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot daily total kWh over testing period\n",
    "y_test_barplot_df = pd.DataFrame(y_test_df,columns=['USAGE'])\n",
    "y_test_barplot_df['Predicted'] = predict_y['USAGE']\n",
    "\n",
    "fig = plt.figure(figsize=[10,4])\n",
    "ax = fig.add_subplot(111)\n",
    "y_test_barplot_df.resample('d',how='sum').plot(kind='bar',ax=ax,color=[gray_light,red_orange])\n",
    "ax.grid(False)\n",
    "ax.set_ylabel('Total Daily Electricity Usage (kWh)', fontsize=fontsize)\n",
    "ax.set_xlabel('')\n",
    "# Pandas/Matplotlib bar graphs convert xaxis to floats, so need a hack to get datetimes back\n",
    "ax.set_xticklabels([dt.strftime('%b %d') for dt in y_test_df.resample('d',how='sum').index.to_pydatetime()],rotation=0, fontsize=fontsize)\n",
    "legend(fontsize=fontsize)\n",
    "plt.show()\n",
    "\n",
    "#fig.savefig('SVM_predict_DailyTotal.png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Error measurements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following are some accuracy measures.  These are moved to the file errors.py for reuse across other models.  \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Number of observations (hours) in test dataset:  168 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import errors\n",
    "\n",
    "N_test = len(y_test_df)\n",
    "print '\\nNumber of observations (hours) in test dataset: ', N_test, '\\n'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Root Mean Sqaure Error**    \n",
    "This is effectively the standard error of the model, and is in the same units as those of the predicted variable (or kilowatt-hours here).  \n",
    "\n",
    "$$RMSE=\\sqrt{\\frac{\\sum_{1}^{N}(y-\\hat{y})^{2}}{N}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RMSE = 0.15 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jelszasz\\AppData\\Local\\Enthought\\Canopy\\User\\lib\\site-packages\\pandas\\core\\frame.py:3083: FutureWarning: TimeSeries broadcasting along DataFrame index by default is deprecated. Please use DataFrame.<op> to explicitly broadcast arithmetic operations along the index\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "RMSE = errors.calc_RMSE(predict_y, y_test_df)\n",
    "print '\\nRMSE =', round(RMSE, 2), '\\n'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mean Absolute Percent Error**  \n",
    "With this measure, the absolute percent error between each predicted value and actual value is calculated and the average is taken; the unit of measure is a percentage. If the absolute value wasn’t taken and there was little bias in the model, you’d end up with something close to zero and the measure would be worthless.  \n",
    "\n",
    "$$MAPE=\\frac{1}{N}\\sum_{1}^{N}\\frac{abs(y-\\hat{y})}{y}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE = 0.16\n"
     ]
    }
   ],
   "source": [
    "MAPE = errors.calc_MAPE(predict_y, y_test_df)\n",
    "print 'MAPE =', round(MAPE, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mean Bias Error**  \n",
    "The mean bias error gives an indication of how the model over- or underestimates.  The mean bias error for the initial SVM model is -0.02, which indicates the model isn’t systematically over- or underestimating the hourly kilowatt-hour consumption.  \n",
    "\n",
    "$$MBE=\\frac{\\frac{1}{N-1}\\sum_{1}^{N}(y-\\hat{y})}{\\bar{y}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "MBE = 0.02 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "MBE = errors.calc_MBE(predict_y, y_test_df)\n",
    "print '\\nMBE =', round(MBE, 2), '\\n'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Coefficient of Variation**  \n",
    "This is similar to the RMSE measure, except it is normalized to the mean. It indicates how much variation there is with respect to the mean.  \n",
    "\n",
    "$$CV=\\sqrt{\\frac{\\frac{1}{N-1}\\sum_{1}^{N}(y-\\hat{y})^{2}}{\\bar{y}}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CV = 0.19 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "CV = errors.calc_CV(predict_y, y_test_df)\n",
    "print '\\nCV =', round(CV, 2), '\\n'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot actual vs. prediced usage.  If prediction were perfect, all points would be on 45 degree line (y=x).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xea4b6d8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RyTAzYyHf2k7sC9+xkP/Qu4h2daSvCc6yX+Be/KLbVaGaAFJdof9vOedeDlwLXIydU/87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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xea4fb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "fontsize = 16\n",
    "plot = plt.plot(y_test_df,predict_y,color=red_orange,marker='.',linewidth=0,markersize=20,alpha=.4)\n",
    "plot45 = plt.plot([0,2],[0,2],'k')\n",
    "plt.xlim([0,2])\n",
    "plt.ylim([0,2])\n",
    "plt.xlabel('Actual Hourly Elec. Usage (kWh)', fontsize=fontsize)\n",
    "plt.ylabel('Predicted Hourly Elec. Usage (kWh)', fontsize=fontsize)\n",
    "\n",
    "#fig.savefig('SVM_plot_errors.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
